College of Sciences


Mathematics and Statistics

Graduate Level


Graduate Program/Concentration

Computational and Applied Mathematics/Statistics

Publication Date





Recent advancements in high-throughput technologies have enabled simultaneous inference of thousands of genes. With the abundance of public databases, it is now possible to rapidly access the results of several genomic studies, each of which includes the significance testing results of a large number of genes. Researchers frequently aggregate genomic data from multiple studies in the form of a meta-analysis. Most traditional meta-analysis methods aim at combining summary results to find signals in at least one of the studies. However, often the goal is to identify genes that are differentially expressed in a consistent pattern across multiple studies. Recently, a meta-analysis method based on the summaries of weighted ordered p-values (WOP) has been proposed that aim at detecting significance in a majority of studies. In the presentation, we will discuss how adherence to the standard null distributional assumptions of the WOP meta-analysis method can lead to incorrect significance testing results. To overcome this, we will propose a robust meta-analysis method that performs an empirical modification of the individual p-values before combining them through the WOP approach. Through various simulation studies, we will show that our proposed meta-analysis method outperforms the WOP method in terms of accurately identifying the truly significant set of genes by reducing false discoveries, especially in the presence of unobserved confounding variables. We will illustrate the application of our method on three sets of micro-array data on lung cancer, brain cancer, and diabetes.


Meta-analysis, Simultaneous inference, Empirical modification, Weighted ordered p-values, Confounding variables, Micro-array data





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Empirically Adjusted Weighted Ordered P-values Method

Included in

Biostatistics Commons